Geisinger Health System introduces an on-demand patient meal service with 98% Service-Level Agreement guarantee using SIMUL8

Patient food service is one aspect of healthcare which is often scrutinized, becoming a central issue for hospital administrators in recent years.

Nutritional intake from food can lead to speedier recovery times and decrease duration of stay, which in turn has a huge impact on hospital running costs.

As well as having a pivotal role in the healing process, the quality of hospital food has been found to significantly influence patient satisfaction with their entire hospital experience.

Administrators have become particularly focused on food in recent years as a result of Medicare taking patient satisfaction performance into account in its payments to hospitals.

Combined with increased competition, made more transparent with the use of publicly available, standardized patient surveys, it’s unsurprising that hospitals are becoming more focused on satisfaction in a bid to boost referrals and ultimately, profitability.

Adopting a hotel-style “room service” has been shown to provide other valuable benefits for hospitals, including a reduction in food costs and waste. One hospital reduced their food costs by $400,000 within the first year of implementing such a system.

Did you know? According to the Environmental Protection Agency, U.S. hospitals generate about 170,000 tons of food waste every year.

It’s clear that there are obvious benefits for hospitals to deliver quality, customer-focused food services. So why is this such a difficult idea for hospitals to implement?

The difficulty lies in striking the balance between achieving patient satisfaction whilst also maximizing use of, often limited, hospital resources and staff.

This is where simulation helped Geisinger Health System to optimize their food service.

“How do we ensure a guaranteed service level – and what should that level be?”

Phase one: simulating the new system

Identifying the parameters for testing

To assess potential constraints on the system, Geisinger identified three key parameters to rigorously test and verify using SIMUL8’s simulation software:

Service time expectation: how often patients received their meal within a set time.

Resource utilization: how quickly delivery carts should be dispatched to maximize their capacity, as well as the number of carts that which would be required over a day.

Staff efficiency: the total number of trips made in a day and the staff resource which would be required.

“The reason we went down this path, to look at the entire hospital, was to understand the overlaps of departments and how we could integrate trips to remove waste throughout the entire system. We wanted to take a more global view of support services rather than looking at each department individually.”

Phase two: using simulation to assess resource needs

“The management team wanted to be able to promise a 45 minute food delivery turnaround for patients.

How did we figure out how to meet that? Resource utilization. Time resources are in use, with resources being staff, and the total number of schedule work hours in a day.”

Introducing real-world constraints

In phase two of the simulation, Geisinger’s management team sought to build on the initial results by introducing realistic constraints. These included existing staff schedules, food preparation time and cart cleaning time.

This would allow for results which were more representative, on which important decisions about the service’s balance between patient expectations and staff efficiency could be based.

Using SIMUL8 to easily import existing staff schedules into the simulation, Geisinger were able to identify an issue with staff utilization.

“We quickly realized there’s no way that they’re going to be able to handle this new system with their current staffing.

I think the utilization was around 150% of staff. We knew that wasn’t feasible, so we needed to provide recommendations for a new schedule.”

Meeting the service level challenge

With the insight from both phases of the simulation taken into account, Geisinger’s management team made the decision to aim for a 45 minute delivery turnaround.

The challenge for the Seth and his team was to determine the optimal staffing schedule, as well as the most efficient delivery and pickup methods, in order to achieve this service level.

To meet this challenge, phase two of the simulation would incorporate the efficiency of combined trips – that is, taking out the fresh meals and collecting dirty trays for cleaning on the same return trip.

Internal zoning models and new, hourly staffing schedules were incorporated to devise and test multiple scenarios for the most efficient pick-up and delivery routes.

Three 24-hour simulation scenarios were run, including a standard three zone fixed route (option A), a modified pick up schedule covering three zones, a rotational route (option B), and an additional six-zone facility layout fixed route (option C).

Seth and his team were surprised by the results of the scenario testing.

It had initially been expected that option B, the rotational schedule, would provide the optimum service level, however, it was in fact option C - the six-zone layout - which delivered the best results.

Using this six-zone layout in the simulation, 98% of meals were delivered within the agreed service level of 45 minutes.

A new work schedule was also identified which provided a balanced, effective employee utilization of 88.4%.

“You really want to target 85 to 90% utilization from your staff. None of us work at 100% although we might think we do! And so that’s what we were really not looking to go much over 90% utilization in anything we would recommend.”

Using the powerful insight gained from healthcare simulation, and the ability to incorporate Geisinger’s real-world data, Seth and his team were able to help the organization’s senior stakeholders make hugely important operational decisions around the implementation.

“The management team were very happy with the results. They were pleased that we had a solid starting point for implementation.”

SIMUL8 was the ideal solution to systematically test, investigate and evaluate throughout the planning and development of the new on-demand food delivery service.

It enabled Geisinger to achieve not just a viable on-demand food delivery simulation, reflective of real-life conditions, but a long-term strategy which delivered on the objective of improving patient satisfaction whilst maximizing staff resource cost effectively.